{"id":"W2111806784","doi":"10.1109/deec.2005.25","title":"Using semantic information to improve transparent query caching for dynamic content Web sites","year":2005,"lang":"en","type":"article","venue":"","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Query optimization; Query expansion; Sargable; Cache; Web search query; Web query classification; Information retrieval; Benchmark (surveying); Query language; Database; Spatial query; Dynamic web page; World Wide Web; Search engine; Web page; Operating system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002373103,0.0001274719,0.0001438816,0.0001500136,0.0001496564,0.0002361222,0.0003242081,0.00003681929,0.000003186893],"category_scores_gemma":[0.00002778726,0.0001123791,0.0001035166,0.0001111488,0.000007398951,0.001361127,0.00006442754,0.00007488328,0.00003263382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000132686,"about_ca_system_score_gemma":0.00005167029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002399642,"about_ca_topic_score_gemma":0.000372215,"domain_scores_codex":[0.9990412,0.00001894632,0.0003009608,0.0001982386,0.000175177,0.0002654659],"domain_scores_gemma":[0.9994302,0.00005794121,0.00006289261,0.0002626653,0.00009373783,0.00009254579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007816141,0.0001535235,0.0008342043,0.0001599492,0.00009174862,0.000002798482,0.003757432,0.1106788,0.5754756,0.01470676,0.0005970048,0.293464],"study_design_scores_gemma":[0.0003993,0.00006744503,0.0002034123,0.00003127398,0.00001142631,0.000006988236,0.0001045367,0.9967452,0.001552477,0.00005215576,0.0006506805,0.0001750837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3504025,0.00003103975,0.6476141,0.001221464,0.0002026144,0.0002656082,0.000006864314,0.0001284463,0.0001273783],"genre_scores_gemma":[0.9694045,0.000003699801,0.02848342,0.00185575,0.00004400657,0.00002044865,0.000007676169,0.000005658807,0.0001748507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8860664,"threshold_uncertainty_score":0.4582688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05944628871965589,"score_gpt":0.2803099796013074,"score_spread":0.2208636908816515,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}